• The Journal of the Acoustical Society of Korea
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• v.27 no.1E
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• pp.1-10
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• 2008

A noise suppression algorithm is proposed for nonstationary noisy environments. The proposed algorithm is different from the conventional approaches such as the spectral subtraction algorithm and the minimum statistics noise estimation algorithm in that it classifies speech and noise signals in time-frequency bins. It calculates the ratio of the variance of the noisy power spectrum in time-frequency bins to its normalized time-frequency average. If the ratio is greater than an adaptive threshold, speech is considered to be present. Our adaptive algorithm tracks the threshold and controls the trade-off between residual noise and distortion. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of simplicity and light computational load for estimating the noise. This algorithm reduces the residual noise significantly, and is superior to the conventional methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.111-117
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• 2007

This paper presents a novel approach to speech enhancement based on a complex Laplacian probability density function (pdf). With a use of goodness-of-fit (GOF) test we show that the complex Laplacian pdf is more suitable to describe the conventional Gaussian pdf. The likelihood ratio (LR) is applied to derive the speech absence probability in the speech enhancement algorithm. The performance of the proposed algorithm is evaluated by the objective test and yields better results compared with the conventional Gaussian pdf-based scheme.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.7
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• pp.1380-1386
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• 2003

Speech recognition performance is degraded by the variation in vocal tract length among speakers. In this paper, we have used a vocal tract length normalization method wherein the frequency axis of the short-time spectrum associated with a speaker's speech is scaled to minimize the effects of speaker's vocal tract length on the speech recognition performance In order to normalize vocal tract length, we tried several frequency warping functions such as linear and piece-wise linear function. Variable interval piece-wise linear warping function is proposed to effectively model the variation of frequency axis scale due to the large variation of vocal tract length. Experimental results on TIDIGITS connected digits showed the dramatic reduction of word error rates from 2.15% to 0.53% by the proposed vocal tract normalization.

• Phonetics and Speech Sciences
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• v.13 no.3
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• pp.21-29
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• 2021

The present study investigated how one's cognitive resources are related to speech perception by examining Korean speakers' executive function (EF) capacity and its association with voice onset time (VOT) and f0 sensitivity in identifying Korean stop laryngeal categories (/t'/ vs. /t/ vs. /t^h/). Previously, Kong et al. (under revision) reported that Korean listeners (N = 154) in Seoul and Changwon (Gyeongsang) showed differential group patterns in dialect-specific cue weightings across educational institutions (college, high school, and elementary school). We follow up this study by further relating their EF control (working memory, mental flexibility, and inhibition) to their speech perception patterns to examine whether better cognitive ability would control attention to multiple acoustic dimensions. Partial correlation analyses revealed that better EFs in Korean listeners were associated with greater sensitivity to available acoustic details and with greater suppression of irrelevant acoustic information across subgroups, although only a small set of EF components turned out to be relevant. Unlike Seoul participants, Gyeongsang listeners' f0 use was not correlated with any EF task scores, reflecting dialect-specific cue primacy using f0 as a secondary cue. The findings confirm the link between speech perception and general cognitive ability, providing experimental evidence from Korean listeners.

• The Journal of the Acoustical Society of Korea
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• v.26 no.1
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• pp.8-15
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• 2007

Speech intelligibility of the eavesdropping sound is investigated on a acoustic cavity - glass window coupled system. Using MLS (Maximum Length Sequency) signal as a sound source, acceleration and velocity responses of the glass window are measured by accelerometer and laser doppler vibrometer. MTF (Modulation Transfer Function) is used to identify tile speech transmission characteristics of the cavity and window system. STI (Speech Transmission Index) based upon MTF is calculated and speech intelligibility of the vibration sound of the glass window is estimated. Speech intelligibilities by the acceleration signal and the velocity signal are compared. Finally, intelligibility of the conversation sound is confirmed by the subjective test.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.818-827
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• 2008

In this paper, we propose a new noise estimation and reduction algorithm for stationary and nonstationary noisy environments. This approach uses an algorithm that classifies the speech and noise signal contributions in time-frequency bins. It relies on the ratio of the normalized standard deviation of the noisy power spectrum in time-frequency bins to its average. If the ratio is greater than an adaptive estimator, speech is considered to be present. The propose method uses an auto control parameter for an adaptive estimator to work well in highly nonstationary noisy environments. The auto control parameter is controlled by a linear function using a posteriori signal to noise ratio(SNR) according to the increase or the decrease of the noise level. The estimated clean speech power spectrum is obtained by a modified gain function and the updated noisy power spectrum of the time-frequency bin. This new algorithm has the advantages of much more simplicity and light computational load for estimating the stationary and nonstationary noise environments. The proposed algorithm is superior to conventional methods. To evaluate the algorithm's performance, we test it using the NOIZEUS database, and use the segment signal-to-noise ratio(SNR) and ITU-T P.835 as evaluation criteria.

• MALSORI
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• no.65
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• pp.153-168
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• 2008

We propose a new method to apply shape analysis techniques to a computational auditory scene analysis (CASA)-based speech separation system. The conventional CASA-based speech separation system extracts speech signals from a mixture of speech and noise signals. In the proposed method, we complement the missing speech signals by applying the shape analysis techniques such as labelling and distance function. In the speech separation experiment, the proposed method improves signal-to-noise ratio by 6.6 dB. When the proposed method is used as a front-end of speech recognizers, it improves recognition accuracy by 22% for the speech-shaped stationary noise condition and 7.2% for the two-talker noise condition at the target-to-masker ratio than or equal to -3 dB.

• Journal of Biomedical Engineering Research
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• v.11 no.1
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• pp.75-82
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• 1990

The purpose of this study is to help the hearing Impaired's speech training with a visible speech processing system. In brief, this system converts the features of speech signals into graphics on monitor, and adjusts the features of hearing impaired to normal ones. There are formant and pitch in the features used for this system. They are extracted using the digital signal processing such as linear predictive method or AMDF(Average Magnitude Difference Function). In order to effectively train for the hearing impaired's abnormal speech, easilly visible feature has been being studied.

• ETRI Journal
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• v.32 no.1
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• pp.160-162
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• 2010

For the acoustic models of embedded speech recognition systems, hidden Markov models (HMMs) are usually quantized and the original full space distributions are represented by combinations of a few quantized distribution prototypes. We propose a maximum likelihood objective function to train the quantized distribution prototypes. The experimental results show that the new training algorithm and the link structure adaptation scheme for the quantized HMMs reduce the word recognition error rate by 20.0%.

• Proceedings of the Acoustical Society of Korea Conference
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• 1994.06a
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• pp.937-942
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• 1994

Text-to-Speech(TTS) conversion system can convert any words or sentences into speech. To synthesize the speech like human beings do, careful prosody control including intonation, duration, accent, and pause is required. It helps listeners to understand the speech clearly and makes the speech sound more natural. In this paper, a prosody control scheme which makes use of the information of the function word is proposed. Among many factors of prosody, intonation, duration, and pause are closely related to syntactic structure, and their relations have been formalized and embodied in TTS. To evaluate the synthesized speech with the proposed prosody control, one of the subjective evaluation methods-MOS(Mean Opinion Score) method has been used. Synthesized speech has been tested on 10 listeners and each listener scored the speech between 1 and 5. Through the evaluation experiments, it is observed that the proposed prosody control helps TTS system synthesize the more natural speech.